To be specific, we inject the global and local context into an existing one-step person search model, i.e., SeqNet [1]}, where two levels of context information work as the additional features to enhance the re-ID embeddings of the target person. The feature enhancement is performed in terms of three hierarchical stages as follows. 1) In the first stage, we design a context enhancer to learn more enhanced context features by playing with the context features itself. 2) The second stage employs the norm aware embedding (NAE) layer [2]} to improve the diversity for re-ID and context features. 3) Finally, in the third stage, we simply fuse all the features and feed them to the online instance matching (OIM) loss [3]} for the ultimate feature interactions and supervising the training process. More concretely, the features are regarded as the memory for the target, and the scenes where he/she often appears and the surrounding concurrent people will be recorded and updated. In this way, re-ID embeddings can perceive the rich context features and vice versa, leading to the final enhanced features, significant improving the person search performance.
